Senior Manager, Machine Learning Operations (Perception)
- Drives the development and scaling of ML Ops infrastructure, including data pipelines, model training and validation workflows, deployment systems, and monitoring frameworks.
- Coordinates cross-functional efforts with perception, controls, and platform teams to ensure seamless integration of ML models into production robotic systems.
- Leads end-to-end ML lifecycle processes, including dataset management, model versioning, release processes, and production validation across distributed fleets.
- Manages and mentors a team of ML and data engineers, fostering a high-performing, collaborative, and execution-focused team environment.
- Ensures high-quality, reliable delivery of ML-enabled functionality by establishing best practices in reproducibility, observability, and operational excellence.
- Drives continuous improvement of monitoring systems, including software and hardware telemetry, to support system performance, diagnostics, and maintenance operations.
- Requires a Bachelor’s Degree in Computer Science, Computer Engineering, Robotics, or a related field; Master’s Degree preferred or equivalent work experience.
- Minimum of 10+ years of experience in software engineering, machine learning systems, or infrastructure development.
- Minimum of 3+ years of experience managing or leading engineering teams in ML, data, or infrastructure domains.
- Strongly preferred{{:}} Experience building and scaling ML Ops platforms, including data pipelines, model deployment systems, and monitoring and observability tools.
- Required technical skills{{:}} Proficiency in Python and/or C++; experience with distributed systems and cloud platforms (e.g., GCP), containerization (Kubernetes), and data/streaming technologies (Kafka, Snowflake). Experience with analytics and visualization tools (e.g., Tableau) is preferred.
- Competencies{{:}} Strong cross-functional leadership, execution focus, communication skills, and the ability to drive process maturity in complex, fast-paced engineering environments.
- Up to 10% of travel may be required. Employees must have a valid driver’s license and the ability to drive and/or fly to client and other customer locations.
- The employee is responsible for owning a credit card and managing expenses personally to be reimbursed on a bi-weekly basis.
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